AI, Ethical AI, and Open Source AI


To learn about the current state of AI, including the total amount of enterprise investment for the last five years (2015-2019), companies that are vocal about ethical AI, and the open source environment including marketplaces focusing on AI as well as what the open source community is passionate about generally.

Early Findings

Investment in AI
  • According to Forbes "State of AI in the Enterprise, 2018,"
  • 82% of enterprise AI early adopters are seeing positive ROI from their production-level projects this year.
  • 63% of enterprises have adopted machine learning, making it the most popular AI technology in 2018.
Ethical AI
  • Replacing biased AI with ethical AI is a hot topic with the artificial intelligence community. It is a main topic of conversation at conferences such as the Neural Information Processing Systems (NeurIPS) which is held annually in Vancouver, Canada.
  • Technical solutions to removing bias in AI systems are often unsuccessful because the data sets used to create AI algorithms are rarely, if ever, bias-free.
  • Currently, "there’s no industry-standard, best-practices handbook on AI ethics for companies to follow"
  • Large companies, including Microsoft and Google, are developing their own internal ethical AI frameworks.
  • Microsoft is one of many companies and think tanks or other organizations that aren't just talking about the importance of ethical AI, they are leading the way in trying to develop AI programs and algorithms that are ethical.
  • In 2018, Microsoft's president and chief legal officer Brad Smith and vice president AI Harry Shum published “The Future Computed: Artificial Intelligence and Its Role in Society.” The book emphasizes the need to build the public's trust in AI. The core principles in the book, principles intended to foster the development of ethical AI, include:
  • Democratizing AI development by opening up the development process thereby distributing the responsibility for ensuring the development of ethical AI across a wider spectrum of developers.
  • Inviting end-users into the development process so developers will have a better understanding of who will be using the final product, who will be directly impacted by the AI. This will help the developers to be able to better understand and address potential barriers and biases that could exclude certain groups of consumers before the final product is released.
  • Make decision-making related to the development of AI transparent, so end-users can see how those decisions were made.
  • Facebook is heavily invested in AI and in open-source machine learning. Soumith Chintala, a researcher at Facebook who works with both AI and open-source machine learning, is considered a top expert. He regularly posts about AI, open source machine learning, and industry trends on his Twitter Feed. He was named a top AI influencer to follow in Twitter by IBM in 2017 and by Medium in 2019.
  • Akira AI is an open-source AI marketplace.
Summary of the Findings Relevant to the Goals
  • During our first hour of our research we were able to find information on enterprise use of AI and the returns it is bringing, ethical AI, including one large company that is working to remove the bias from AI, making it more ethical. We were also able to find a researcher for Facebook that is a vocal, passionate member of both the AI community and the open source community, especially as it relates to machine learning.
  • We did not have time to look for the number of open source marketplaces.

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